Typically, real-world optimization problems include objective functions that are based on the output of one or more simulation models. In this case, the underlying processes of the problems may be time and computation intensive, and the objective function is deemed expensive (i.e., central processing unit intensive) to evaluate. While methods to alleviate this cost in the optimization procedure have been explored, utilizing neural networks, splines, response surface methods and radial basis function approximations amongst others, less attention has been given to the treatment of expensive constraints.